The goal of the single-person pose estimation method based on deep learning is to determine key points of a human body part. It is the first open-source online pose tracker that achieves both 60+ mAP (66. 360° 6D HeadPose detection. The reason for its importance is the abundance of applications that can benefit from such a technology. We take 2D images as our research object in this paper, and propose a 3D pose estimation model called Pose ResNet. The model consisted of an AlexNet backend (7 layers) with an extra final layer that outputs 2k joint coordinates. Remember that to run GPU-dependent apps on your machine you need to connect a computer with a GPU to your Supervisely account. {Hidalgo Martinez} and T. From the above experiments, we found that the result of level1 is the best. This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. With the Coral Edge TPU™, you can run a pose estimation model directly on your device, using real-time video, at over 100 frames per second. Nov 3, 2021 · November 15, 2021 — Posted by Valentin Bazarevsky,Ivan Grishchenko, Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir, Jiuqiang Tang,Jason Mayes, Ahmed Sabie, Google Today, we're excited to share a new version of our model for hand pose detection, with improved accuracy for 2D, novel support for 3D, and the new ability to predict keypoints on both hands simultaneously. Typical single-person pose estimation models are divided into two types: the framework based on direct regression which directly return the key points from the features and the heat map-based framework which generate a heat map first Apr 25, 2022 · Figure. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. Best Model Paper rwightman/pytorch-image-models • • 1 Jul 2019. However, prior compositional models make unrealistic assumptions on subpart-part relationships, making them incapable to characterize complex compositional patterns. {Cao} and G. Somewhere in-between there are solvers that simultaneously use different types of matches, referred to as hybrid solvers, although they have not gained much attention in the past. In the case of human pose estimation, we typically accomplish this by estimating the locations of various key points like hands, heads, elbows, and so on. This provides flexibility to select the right model for different speed and Oct 12, 2017 · Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019 Nov 19, 2022 · Human pose estimation (HPE) has developed over the past decade into a vibrant field for research with a variety of real-world applications like 3D reconstruction, virtual testing and re-identification of the person. Although it is impossible to cover a wide range of models, we will discuss some of the most reliable and robust models proposed with Mar 12, 2023 · The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. In simple terms, a human pose estimation model takes in an image or video and estimates the position of a person’s skeletal joints in either 2D or 3D space. Predict with pre-trained AlphaPose Estimation models; 3. First, a face is detected and only then can head pose be estimated. The model outputs an estimate of 33 3-dimensional pose landmarks. Apr 2, 2021 · PoseNet is an ML model that allows for real-time human pose estimation by finding different points on the human body and face. Background. the answer was a resounding "I'd give up depth; don't take away my color!" That's a big change from just a few years ago. Therefore, we present this survey article to fill the knowledge gap and This task targets at 3D human pose estimation with a single RGB camera. For You signed in with another tab or window. Fusion of SOTA models. In this respect, the selection of the frames with the best face orientation can allow triggering recognition Apr 21, 2022 · Pose Estimation Models. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. YOLOv7 pose is unique, as it deviates from the conventional 2-stage pose estimation algorithms. 6M (Number of Frames Per View metric) We set up the MPI-INF-3DHP dataset following P-STMO. Both the Object Detection models and the Pose Estimation models have the same backbone and neck design but differ in the head. Mar 14, 2023 · 2. Consequently, numerous metrics have been developed to assess both 2D and 3D pose estimation methods. However, existing bottom-up pose estimation models [5, 8,11,21,22,24,37,38,40,41] mainly focus on the high-computation region. The experimental results are shown in Table 6. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. You can even run a second model concurrently on one Edge TPU, while maintaining a high frame rate. #13 best model for Weakly-supervised 3D Human Pose Estimation on Human3. , images, videos, or signals). X 3d human pose estimation: A review of the literature and analysis of covariates [145] 2016 CVIU A review of the advances in 3D human pose estimation from RGB images or image Feb 20, 2023 · Human Pose Estimation (HPE) is a way of capturing 2D and 3D human movements using labels and annotations to train computer vision models. cwc1260/handdiff • • CVPR 2024 Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications. The model is light enough to be used with portable devices . Several studies Dec 15, 2023 · Pose Estimation is still a pretty new computer vision technology. Unlike existing instance-level or category-level methods, OnePose does not rely on CAD models and can handle objects in arbitrary categories without instance- or category-specific network training. The complete guide on automatic body pose estimation of animals and humans on your images in Supervisely📗 Guide in the blogpost: https://supervisely. C-3PO first detects semantic keypoints from the input, partial point cloud using a trainable regression model. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). Hybrid pose estimation. 1 Single-person pose estimation. In this paper, we study efficient architecture design for real-time multi-person pose estimation on edge. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. This chapter shows you step by step how to write the code to recognize various poses (keypoints) of the human face. Nov 12, 2023 · YOLOv8 pose models use the -pose suffix, i. In this study, we propose a novel solution by reframing Oct 13, 2021 · The phenomenon of Human Pose Estimation (HPE) is a problem that has been explored over the years, particularly in computer vision. We propose C-3PO (Certifiable 3D POse) to solve the certifiable object pose estimation problem (Section V). Here is the mapping of each index to its respective body joint: Sep 17, 2016 · As argued before, strong part detectors are an essential ingredient of modern pose estimation methods. Their ability to characterize high-order relationships among body parts helps resolve low-level ambiguities in human pose estimation (HPE). We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Human Pose Estimation. ⚔️ We release a series of models named DWPose with different sizes, from tiny to large, for human whole-body pose estimation. Source. As recently as 2019, in the Benchmark for 6D Object Pose Estimation (a nearly annual competition), geometric pose estimation was still outperforming deep-learning based approaches Hodan20. g. However, our training/testing data is different from theirs. The advent of deep learning has significantly improved the accuracy of pose capture, making pose-based applications Apr 5, 2024 · The YOLOv8x-pose human pose estimation model inherits the basic idea of feature fusion module from YOLOv5x 42 in the neck part and uses the Path Aggregation Network (PANet) 18 as the processing End-to-end Recovery of Human Shape and Pose. To correct for potential keypoint- Nov 11, 2022 · Human Pose Estimation (HPE) is a powerful tool when machine learning models are applied to image and video annotation. The task contains two directions: 3-D gaze vector and 2-D gaze position estimation. These algorithms cannot capture a person’s complete range of motion, so more complex body positions will not be detected correctly. OnePose draws the idea from visual localization and only requires a simple RGB video scan of the object to build a sparse SfM model **Monocular Depth Estimation** is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. {Sheikh}}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, title = {OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields}, year = {2019} } @inproceedings{simon2017hand, author = {Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh Mar 22, 2023 · Human Pose Estimation (HPE) is a powerful way to use computer vision models to track, annotate, and estimate movement patterns for humans, animals, and vehicles. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel Dec 3, 2023 · 2D pose estimation simply estimates the location of keypoints in 2D space relative to an image or video frame. They train and evaluate on 3D poses scaled to the height of the universal skeleton used by Human3. First, the model uses ResNet50 as the base network and introduces the attention mechanism CBAM to extract features. Gaze Estimation is a task to predict where a person is looking at given the person’s full face. In this paper, we discuss some of the methods used for human pose estimation to come up with a better method Mar 18, 2024 · The objective of Pose Estimation, a general problem in computer vision, is to identify the location and orientation of an item or human. But what exactly is it? To answer this, the concept of a pose must first be understood. , upper/full human body joints, single/multiple pose estimation, human body scale). 2-D gaze position estimation is to predict the horizontal and vertical coordinates on a 2-D screen, which Pose Estimation. Papers With Code provides a comprehensive list of papers and code for this task, as well as benchmarks and leaderboards. This review focuses on the key aspects of One drawback of 2D pose estimation, however, is that it can only represent the human body in a 2-dimensional space. Jun 18, 2023 · Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings. Human pose estimation is a growing technique and of particular benefit to clinicians and researchers working in musculoskeletal biomechanics interested in calculating human movement patterns (Stenum et al 2021). While recent research primarily aims at enhancing estimated pose performance, it is important to acknowledge the challenges encountered when evaluating these estimations against ground truth pose data. 6M (officially called "univ_annot3"), while we use the ground truth 3D poses (officially called "annot3"). Two different setups are 76. Jun 5, 2024 · Best Model Paper Code Compare; NYU Hands Use these libraries to find Hand Pose Estimation models and implementations open-mmlab/mmpose 3 papers Jun 20, 2024 · Pose estimation aims to accurately identify anatomical keypoints in humans and animals using monocular images, which is crucial for various applications such as human-machine interaction, embodied AI, and autonomous driving. It is a vital advance toward understanding individuals in videos and still images. 6M. 3D Human Pose Estimation What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Dive deep into Training a Simple Pose Model on COCO Keypoints; Action Recognition. Then we conduct a pose estimation with the best performing subject in the 3DHP_2 for different actions. It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields. This disadvantage can be mitigated using 3D human pose estimation models. BodyPoseNet Model Card Model Overview . Due to its widespread applications in a great variety of areas, such as human motion analysis, human–computer interaction, robots, 3D human pose estimation has recently attracted increasing attention in the computer vision community, however, it is a HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud. yolov8n-pose. All pre-processing and post-processing are fused together, allowing end-to-end processing in a single inference. Meanwhile, we utilize Dual model head pose estimation. The model estimates an X and Y coordinate for each keypoint. AlphaPose supports both Linux and Windows! Nov 7, 2023 · The Pose models are built on top of the YOLO-NAS object detection architecture. Related Work Multi-person 2D pose estimation can be categorized into May 24, 2022 · We propose a new method named OnePose for object pose estimation. Pose estimation comes almost free with an OD network. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. {Wei} and Y. ning the pose estimation model for each person in the image, it is more suitable for real-time multi-person pose estimation on edge. It takes enormous computational… Head pose estimation is a sensitive topic in video surveillance/smart ambient scenarios since head rotations can hide/distort discriminative features of the face. Image Credit: “Microsoft Coco: Common Objects in Context Dataset”, https://cocodataset. com/blo To match poses that correspond to the same person across frames, we also provide an efficient online pose tracker called Pose Flow. We propose low-complexity variants of our models that significantly outperforms real-time focused models like EfficientHRNet [15]. Release RTMO, a state-of-the-art real-time method for multi-person pose estimation. It is a single-stage, multi-person pose estimation model. Moreover, previous efforts often study Oct 18, 2022 · YOLOv7 Pose was introduced in the YOLOv7 repository a few days after the initial release in July ‘22. Then, a (II) C-3PO: a Certifiable 3D POse Estimation Model. The Kinematic models can be used for both 2D and 3D pose estimation. 3D pose estimation works to transform an object in a 2D image into a 3D object by adding a z-dimension to the prediction. open-mmlab/mmpose • • CVPR 2018 The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations. Reload to refresh your session. Traditional methods use multi-view geometry to find the relationship between the images. Nov 11, 2022 · Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e. 3. Nov 6, 2020 · Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. While current models show promising results, they are typically trained and tested on clean data, potentially overlooking the corruption during real-world deployment and Proposes a joint detection and pose estimation framework. The comparative study of research approaches in the United States and China, both leaders in this domain, is vital for understanding and influencing global trends in this technology. In the default YOLOv8 pose model, there are 17 keypoints, each representing a different part of the human body. Therefore, we can define the problem of Human Pose Estimation as the localization of human joints or . ducing the total running time of robust pose estimation. Get started If you are new to TensorFlow Lite and are working with Android or iOS, explore the following example applications that can help you get started. We reveal that HRNet Sep 1, 2021 · Three-dimensional (3D) human pose estimation involves estimating the articulated 3D joint locations of a human body from an image or video. Face recognition would often tackle the problem of video frames where subjects appear in poses making it quite impossible. Our code is based on MMPose and ControlNet. Sep 10, 2022 · MediaPipe is based on a deep learning network to provide face and body pose estimation from videos. MediaPipe library contains a general pose estimation model which provides a total of 33 landmarks, 11 of which are used for the face. Mar 5, 2022 · And performed a 3D pose estimation experiment after obtaining the relevant codes of the test set. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis. , detecting a single class object (like a person or an animal) and Aug 3, 2020 · The paper proposed a human pose estimation method based on Deep Neural Networks (DNNs), where the pose estimation was formulated as a DNN-based regression problem towards body joints. Computer vision technology empowers machines to perform highly-complex image and video processing and annotation tasks that imitate what the human eye and mind process in a fraction of a second. Release RTMW models in various sizes ranging from RTMW-m to RTMW-x. A. This page provides several trained models that are compiled for the Edge TPU, and some example code to Multi-person pose estimation is the task of estimating the pose of multiple people in one frame. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. May 26, 2015 · 2. (2018) 2D vs 3D Pose Estimation. In this section, we cover the following topics on training a 2D pose estimation model with TAO Toolkit: Methodology; Environment setup; Data preparation; Experiment configuration file; Training; Evaluation **Depth Estimation** is the task of measuring the distance of each pixel relative to the camera. 1. 3 MOTA) on PoseTrack Challenge dataset. We classify existing instance-level methods into four categories: correspondence-based, template-based, voting-based, and regression-based methods. State-of-the-art methods usually fall into one of two categories: designing a Instance-level object pose estimation describes the task of estimating the pose of the objects that have been seen during the training of the model. 2. Despite the practicality of category-level pose estimation, current approaches encounter challenges with partially observed point clouds, known as the multihypothesis issue. Predict with pre-trained Simple Pose Estimation models; 2. Sep 19, 2023 · Animal pose estimation can be performed by fine-tuning pre-trained YOLOv8 pose models for analyzing animal postures, and performing specific keypoint analysis. Feb 16, 2024 · The evolution of 3D human pose estimation techniques has seen substantial progress over the past few decades, with notable advancements in accuracy and applications. May 21, 2024 · The following models are packaged together into a downloadable model bundle: Pose detection model: detects the presence of bodies with a few key pose landmarks. Estimate pose from your webcam; 4. However, with the progresses in the field The accurate estimation of 3D human pose is of great importance in many fields, such as human-computer interaction, motion recognition and automatic driving. Unlike Release RTMW3D, a real-time model for 3D wholebody pose estimation. 3-D gaze vector estimation is to predict the gaze vector, which is usually used in the automotive safety. We propose and evaluate a deep fully-convolutional human body part detection model drawing on powerful recent ideas from semantic segmentation, object classification [12, 27, 28] and human pose estimation [10, 11, 20]. Various researchers have proposed different pose, estimation models. This review collected 191 influential papers from 2014 to 2022, sourced from Google Scholar. e. In this article, fine-tune the YOLOv8 Pose model for Animal Pose Estimation. 3). These datasets provide standardized evaluation metrics and ground truth annotations, enabling researchers and developers to train and validate pose estimation algorithms for improved accuracy and Oct 19, 2023 · Human pose analysis has garnered significant attention within both the research community and practical applications, owing to its expanding array of uses, including gaming, video surveillance, sports performance analysis, and human-computer interactions, among others. Oct 1, 2021 · To evaluate a pose estimation model, the chosen metric has to consider several factors and features (e. The Structural Topic Model (STM) was cused on the problem of instant pose estimation of arbitrary novel objects [19,32,40,55,58]. In this article, we propose a novel Distribution-Aware Single-stage (DAS) model for the pose-related tasks. The input sizes include 256x192 and 384x288. 2) for novel view RGBD rendering for subsequent render-and-compare. This repository is the official implementation of the Effective Whole-body Pose Estimation with Two-stages Distillation (ICCV 2023, CV4Metaverse Workshop). , pose estimation, body mesh recovery and pose tracking. Oct 12, 2017 · Fast and accurate human pose estimation in PyTorch. Information about human poses is also a critical component in many downstream tasks, such as activity recognition and movement tracking. Before we dive in, it is essential to understand that human pose estimation models are basically of three types: a) kinematic, b) planar, and; c) volumetric. {Simon} and S. These models are trained on the COCO keypoints dataset and are suitable for a variety of pose estimation tasks. pt. Training a 2D Pose Estimation model with TAO Toolkit. You can also find the latest research and methods on hand pose estimation from a single RGB image, which is a challenging and important problem for human-computer Jun 29, 2020 · What are the different techniques used to estimate head pose? Note: Many approaches in head pose estimation assume face detection as a preliminary step. Jun 21, 2024 · We developed a new framework called panoptic pose estimation, where models can be used across various environments in a zero-shot manner and if fine-tuned, they require 10–100× less labeled Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames. But by the 2020 version of the Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. You switched accounts on another tab or window. Human-computer interactions can be enhanced by the applications of human pose estimation. Human pose estimation from monocular images: A comprehensive sur-vey [45] 2016 Sensors A survey of conventional and deep learning methods for human pose estimation. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper. Dec 22, 2023 · Human pose estimation, a key area in computer vision, benefits various fields. org Jul 20, 2020 · Human pose estimation localizes body keypoints to accurately recognizing the postures of individuals given an image. Pose landmarker model: adds a complete mapping of the pose. Deep learning techniques allow learning feature representations directly Mar 25, 2019 · Notice the jitter in Single-frame model and the smoothness in Temporal model. Introducing Decord: an Aug 16, 2022 · The human pose estimation is a significant issue that has been taken into consideration in the computer vision network for recent decades. 9 0 0 Model-free estimation 0 Model-based estimation Model-free tracking Model-based tracking OnePose++ MegaPose FS6D Ours Figure 1. Feb 9, 2024 · Understanding human posture is a challenging topic, which encompasses several tasks, e. Our findings emphasize the Aug 30, 2023 · Pose estimation is the task of using an ML model to estimate the pose of a person from an image or a video by estimating the spatial locations of key body joints (keypoints). The proposed DAS model estimates human position and localizes joints simultaneously, which requires only a single pass. May 30, 2023 · Understanding PoseNet: PoseNet is a deep learning model that utilizes convolutional neural networks (CNNs) to estimate the 2D or 3D pose of a human body from an input image or video frame. [5] uses two 2D-3D matches and one 3D-3D match in order to estimate the pose and scale To bridge the gap between model-free and model-based setup, we leverage an object-centric neural field (Sec. Image courtesy Pavllo et al. You can apply object detection, bounding boxes, pictoral structure framework (PSF), and Gaussian layers, and even using convolutional neural networks (CNN) for segmentation, detection, and classification tasks. The head for YOLO-NAS Pose is designed for its multi-task objective, i. Pose can be defined as the arrangement of human joints in a specific manner. You signed out in another tab or window. 2: Different Types of Pose Estimation Models. Pose estimation, as we have learned, has far-reaching implications in numerous fields including augmented reality, sports analysis, and healthcare. This step is a crucial prerequisite to multiple tasks of computer vision which include human action recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. Getting Started with Pre-trained TSN Models on UCF101; 10. Human pose estimation has tangible benefits in the growing trends in computer vision-based technology. The observation of a patient’s joint angle during a movement can determine the effectiveness of a rehabilitation programme, risk of injury and other quality of life measures (Dos 3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. Best Model Paper Code tensorflow/models • • ICCV 2017 May 7, 2018 · PoseNet returns confidence values for each person detected as well as each pose keypoint detected. Our unified framework enables both 6D pose estimation and Aug 25, 2023 · Explore our blog post ViTPose — How to use the best Pose Estimation Model on Humans & Animals to get in-depth knowledge about this state-of-the-art pose estimation model. 10,11,12,13,14 build 6D pose estimation models directly, and we found that Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. 2D Pose Estimation is predicting the location of body joints in the image (in terms of pixel values). In this section, we mention the most well-known metrics Apr 28, 2023 · In previous chapters, we introduce partial pose estimation networks from template-based to voting-based methods, Ref. However, in recent years, human pose estimation accuracy achieved great breakthroughs with Convolutional Neural Networks (CNNs). The proposed human pose estimation method is based on an SVM (support vector machine) and superpixels. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured. Pose Estimation with OpenPose. For pose estimation, we first initialize global poses uniformly around the object, which are then refined by the refinement network (Sec. Jul 19, 2023 · Popular pose estimation datasets that are widely used for training and evaluating pose estimation models include COCO, MPII Human Pose, and Human3. A human pose skeleton denotes the orientation of an individual in a particular format. This survey will include a set of papers for analysis of performance as well as the occlusion. Despite recent advancements in deep learning-based methods, they mostly ignore the capability of coupling accessible texts and naturally feasible knowledge of humans, missing out on valuable implicit supervision to guide the 3D HPE task. Dec 8, 2023 · This hands-on guide brought to light the key aspects of setting up the environment, understanding pose estimation theory, and eventually implementing a real-time pose estimation model. The BodyPoseNet models described in this card are used for multi-person human pose estimation network, which aims to predict the skeleton for every person in a given input image which consists of keypoints and the connections between them. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose May 3, 2022 · Pose estimation plays a critical role in human-centered vision applications. Our pose estimation method predicts 15 joint positions of the human body: head, neck, torso, L/R (left/right) shoulders, L/R elbows, L/R hands, L/R hips, L/R knees and L/R feet. 5 mAP) and 50+ MOTA (58. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. There are two major approaches used to estimate head pose. One approach involves an intermediate step of estimating facial May 8, 2024 · The 3D Human Pose Estimation (3D HPE) task uses 2D images or videos to predict human joint coordinates in 3D space. Jun 25, 2024 · Lightning Pose is an efficient pose estimation approach that requires few labeled training data owing to its semi-supervised learning strategy and ensembling. For instance, HigherHRNet [8] achieves its best performance on the CrowdPose dataset Jun 10, 2021 · We compare the trained model from TAO Toolkit with other state-of-the-art models. @article{8765346, author = {Z. Depending on the output dimension requirement, the Pose Estimation problem can be classified into 2D Pose Estimation and 3D Pose Estimation. fe ux lc wy ig cr yt tl ay mk